class_docstr: Data type that is fed into and yielded from agent connectors.
  
  Args:
      env_id: ID of the environment.
      agent_id: ID to help identify the agent from which the data is received.
      data: A payload (``data``). With RLlib's default sampler, the payload
          is a dictionary of arbitrary data columns (obs, rewards, terminateds,
          truncateds, etc).
code: def __init__(self, env_id: str, agent_id: str, data: Any):
      self.env_id = env_id
      self.agent_id = agent_id
      self.data = data
  
file_path: ray/python/ray/rllib/utils/typing.py
imports: from typing import TYPE_CHECKING, Any, Callable, Dict, Hashable, List, Optional, Tuple, Type, TypeVar, Union
  import numpy
  import gymnasium
  from ray.rllib.utils.annotations import ExperimentalAPI
label_desc: Use image node_id 1 to create a new AgentConnectorDataType object  with example: obj = AgentConnectorDataType(env_id, agent_id, data)